惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

V
Visual Studio Blog
Stack Overflow Blog
Stack Overflow Blog
G
Google Developers Blog
Microsoft Azure Blog
Microsoft Azure Blog
Engineering at Meta
Engineering at Meta
L
LangChain Blog
T
The Blog of Author Tim Ferriss
J
Java Code Geeks
Y
Y Combinator Blog
月光博客
月光博客
雷峰网
雷峰网
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
F
Fortinet All Blogs
A
About on SuperTechFans
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
P
Proofpoint News Feed
小众软件
小众软件
H
Help Net Security
Last Week in AI
Last Week in AI
B
Blog RSS Feed
宝玉的分享
宝玉的分享
N
Netflix TechBlog - Medium
博客园 - 叶小钗
The GitHub Blog
The GitHub Blog

eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
A Digital Twin Platform Enabling Monolithic Crystal-Free ...
Brandon P. Hippe, Jacob N. Louie, Dingyu Zhou, Alfonso Cortés, F · 2026-05-23 · via eess.SP updates on arXiv.org

Low-power wireless-capable systems-on-chips (SoCs) are critical for researching many of our current environmental issues. The scale at which these devices are needed for many applications necessitates innovation in their design to reduce the various capital and labor costs involved with operating an extensive sensor network. This can be difficult for devices with novel wireless architectures, as many emerging architectures lack commercially available development platforms. This makes pre-silicon validation challenging, and the impact of a failed tapeout is unacceptable when the cost is of primary concern for these devices. In this work, we propose a digital twin ecosystem for Bluetooth Low-Energy (BLE) with physical-layer (PHY) control intended for novel device development and demonstrated through use with crystal-free single-chip sensor motes. We present this system operating with multiple RF front ends and digital baseband implementations, including a commercially available Software Defined Radio (SDR) with synthesized RTL and embedded firmware, along with an existing crystal-free SoC front end and FPGA digital baseband. These configurations are shown to be capable of communicating sensor data with commercially available BLE devices and achieving receiver sensitivities up to -82 dBm, exceeding the minimum BLE specification. This approach is extendable to other hardware and communication protocols and promises to enable inexpensive, reusable validation and verification tools for novel wireless devices.